Methods and apparatus for tailoring spectroscopic calibration models

ABSTRACT

A method and apparatus for non-invasively measuring a biological attribute, such as the concentration of an analyte, particularly a blood analyte in tissue such as glucose. The method utilizes spectrographic techniques in conjunction with an improved subject-tailored calibration model. In a calibration phase, calibration model data is modified to reduce or eliminate subject-specific attributes, resulting in a calibration data set modeling within--subject physiological variation, sample location, insertion variations, and instrument variation. In a prediction phase, the prediction process is tailored for each target subject separately using a minimal number of spectral measurements from each subject.

CROSS REFERENCE TO CO-PENDING APPLICATIONS

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 09/170,022, filed Oct. 13, 1998, now abandoned.

TECHNICAL FIELD

The present invention relates generally to methods for multivariatecalibration and prediction and their application to the non-invasive ornon-destructive measurement of selected properties utilizingspectroscopy methods. A specific implementation of the invention relatesto the situation where the multivariate calibration and predictionmethods are utilized in a situation wherein biological tissue isirradiated with infrared energy having at least several wavelengths anddifferential absorption by the biological tissue sample is measured todetermine an analyte concentration or other attribute of the tissue byapplication of the calibration model to the resulting spectralinformation.

BACKGROUND OF THE INVENTION

The need and demand for an accurate, non-invasive method for determiningattributes of tissue, other biological samples or analyte concentrationsin tissue or blood are well documented. For example, accuratenon-invasive measurement of blood glucose levels in patients,particularly diabetics, would greatly improve treatment. Barnes et al.(U.S. Pat. No. 5,379,764) disclose the necessity for diabetics tofrequently monitor glucose levels in their blood. It is furtherrecognized that the more frequent the analysis, the less likely therewill be large swings in glucose levels. These large swings areassociated with the symptoms and complications of the disease, whoselong-term effects can include heart disease, arteriosclerosis,blindness, stroke, hypertension, kidney failure, and premature death. Asdescribed below, several systems have been proposed for the non-invasivemeasurement of glucose in blood. However, despite these efforts, alancet cut into the finger is still necessary for all presentlycommercially available forms of home glucose monitoring. This isbelieved so compromising to the diabetic patient that the most effectiveuse of any form of diabetic management is rarely achieved.

The various proposed non-invasive methods for determining blood glucoselevel generally utilize quantitative infrared spectroscopy as atheoretical basis for analysis. In general, these methods involveprobing glucose containing tissue using infrared radiation in absorptionor attenuated total reflectance mode. Infrared spectroscopy measures theelectromagnetic radiation (0.7-25 μm) a substance absorbs all variouswavelengths. Molecules do not maintain fixed positions with respect toeach other, but vibrate back and forth about an average distance.Absorption of light at the appropriate energy causes the molecules tobecome excited to a higher vibration level. The excitation of themolecules to an excited state occurs only at certain discrete energylevels, which are characteristic for that particular molecule. The mostprimary vibrational states occur in the mid-infrared frequency region(i.e., 2.5-25 μm). However, non-invasive analyte determination in bloodin this region is problematic, if not impossible, due to the absorptionof the light by water. The problem is overcome through the use ofshorter wavelengths of light which are not as attenuated by water.Overtones of the primary vibrational states exist at shorter wavelengthsand enable quantitative determinations at these wavelengths.

It is known that glucose absorbs at multiple frequencies in both themid- and near-infrared range. There are, however, other infrared activeanalytes in the tissue and blood that also absorb at similarfrequencies. Due to the overlapping nature of these absorption bands, nosingle or specific frequency can be used for reliable non-invasiveglucose measurement. Analysis of spectral data for glucose measurementthus requires evaluation of many spectral intensities over a widespectral range to achieve the sensitivity, precision, accuracy, andreliability necessary for quantitative determination. In addition tooverlapping absorption bands, measurement of glucose is furthercomplicated by the fact that glucose is a minor component by weight inblood and tissue, and that the resulting spectral data may exhibit anon-linear response due to both the properties of the substance beingexamined and/or inherent non-linearities in optical instrumentation.

A further common element to non-invasive glucose measuring techniques isthe necessity for an optical interface between the body portion at thepoint of measurement and the sensor element of the analyticalinstrument. Generally, the sensor element must include an input elementor means for irradiating the sample point with the infrared energy. Thesensor element must further include an output element or means formeasuring transmitted or reflected energy at various wavelengthsresulting from irradiation through the input element. The opticalinterface also introduces variability into the non-invasive measurement.

Robinson et al. (U.S. Pat. No. 4,975,581) disclose a method andapparatus for measuring a characteristic of unknown value in abiological sample using infrared spectroscopy in conjunction with amultivariate model that is empirically derived from a set of spectra ofbiological samples of known characteristic values. The above-mentionedcharacteristic is generally the concentration of an analyte, such asglucose, but also may be any chemical or physical property of thesample. The method of Robinson et al. involves a two-step process thatincludes both calibration and prediction steps. In the calibration step,the infrared light is coupled to calibration samples of knowncharacteristic values so that there is differential attenuation of atleast several wavelengths of the infrared radiation as a function of thevarious components and analytes comprising the sample with knowncharacteristic value. The infrared light is coupled to the sample bypassing the light through the sample or by reflecting the light from thesample. Absorption of the infrared light by the sample causes intensityvariations of the light that are a function of the wavelength of thelight. The resulting intensity variations at the at least severalwavelengths are measured for the set of calibration samples of knowncharacteristic values. Original or transformed intensity variations arethen empirically related to the known characteristic of the calibrationsamples using a multivariate algorithm to obtain a multivariatecalibration model. In the prediction step, the infrared light is coupledto a sample of unknown characteristic value, and the calibration modelis applied to the original or transformed intensity variations of theappropriate wavelengths of light measured from this unknown sample. Theresult of the prediction step is the estimated value of thecharacteristic of the unknown sample. The disclosure of Robinson et al.is incorporated herein by reference.

Barnes et al. (U.S. Pat. No. 5,379,764) disclose a spectrographic methodfor analyzing glucose concentration wherein near infrared radiation isprojected on a portion of the body, the radiation including a pluralityof wavelengths, followed by sensing the resulting radiation emitted fromthe portion of the body as affected by the absorption of the body. Themethod disclosed includes pretreating the resulting data to minimizeinfluences of offset and drift to obtain an expression of the magnitudeof the sensed radiation as modified.

Dahne et al. (U.S. Pat. No. 4,655,225) disclose the employment of nearinfrared spectroscopy for non-invasively transmitting optical energy inthe near infrared spectrum through a finger or earlobe of a subject.Also discussed is the use of near infrared energy diffusely reflectedfrom deep within the tissues. Responses are derived at two differentwavelengths to quantify glucose in the subject. One of the wavelengthsis used to determine background absorption, while the other wavelengthis used to determine glucose absorption.

Caro (U.S. Pat. No. 5,348,003) discloses the use of temporally modulatedelectromagnetic energy at multiple wavelengths as the irradiating lightenergy. The derived wavelength dependence of the optical absorption perunit path length is compared with a calibration model to deriveconcentrations of an analyte in the medium.

Wu et al. (U.S. Pat. No. 5,452,723) disclose a method of spectrographicanalysis of a tissue sample which includes measuring the diffusereflectance spectrum, as well as a second selected spectrum, such asfluorescence, and adjusting the spectrum with the reflectance spectrum.Wu et al. assert that this procedure reduces the sample-to-samplevariability.

The intended benefit of using models such as those disclosed above,including multivariate analysis as disclosed by Robinson, is that directmeasurements that are important but costly, time consuming, or difficultto obtain, may be replaced by other indirect measurements that arecheaper and easier to get. However, none of the prior art modelingmethods, as disclosed, has proven to be sufficiently robust or accurateto be used as a surrogate or replacement for direct measurement of ananalyte such as glucose.

Of particular importance to the present invention is the use ofmultivariate analysis. Measurement by multivariate analysis involves atwo-step process. In the first step, calibration, a model is constructedutilizing a dataset obtained by concurrently making indirectmeasurements and direct measurements (e.g., by invasively drawing ortaking and analyzing a biological sample such as blood for glucoselevels) in a number of situations spanning a variety of physiologicaland instrumental conditions. A general form for the relationship betweendirect (blood-glucose concentration) and the indirect (optical)measurements is G=ƒ(y₁, y₂, . . . ,y_(q)), where G is the desiredestimated value of the direct measurement (glucose), ƒ is some function(model), and y₁, y₂, . . . ,y_(q) (the arguments of ƒ) represents theindirect (optical) measurement, or transformed optical measurements, atq wavelengths. The goal of this first step is to develop a usefulfunction, ƒ. In the second step, prediction, this function is evaluatedat a measured set of indirect (optical) measurements {y₁, y₂, . . .,y_(q) } in order to obtain an estimate of the direct measurement(blood-glucose concentration) at some time in the future when opticalmeasurements will be made without a corresponding direct or invasivemeasurement.

Ideally, one would prefer to develop a calibration model that isapplicable across all subjects. Many such systems have been proposed asdiscussed above. However, it has been shown that for many applicationsthe variability of the items being measured makes it difficult todevelop such a universal calibration model. For the glucose application,the variability is across subjects with respect to the opticalappearance of tissue and, possibly, across the analyte within thetissue.

FIG. 1 indicates the levels of spectral variation observed both amongand within subjects during an experiment in which 84 measurements wereobtained from each of 8 subjects. Sources of spectral variation within asubject include: spatial effects across the tissue, physiologicalchanges within the tissue during the course of the experiment, samplingeffects related to the interaction between the instrument and thetissue, and instrumental/environmental effects. The spectral variationacross subjects is substantially larger than the sum of all effectswithin a subject. In this case the subjects were from a relativelyhomogeneous population. In the broader population it is expected thatspectral variation across subjects will be substantially increased.Thus, the task of building a universal calibration model is a dauntingone.

In order to avoid the issue of variability across subjects, one approachinvolves building a completely new model for each subject. Such a methodinvolves a substantial period of observation for each subject, as taughtby R. Marbach et al.,"Noninvasive Blood Glucose Assay by Near-InfraredDiffuse Reflectance Spectroscopy of the Human Inner Lip," AppliedSpectroscopy, 1993, 47, 875-881. This method would be inefficient andimpractical for commercial glucose applications due to the intensiveoptical sampling that would be needed for each subject.

Another approach taught by K. Ward et al., "Post-Prandial Blood GlucoseDetermination by Quantitative Mid-Infrared Spectroscopy," AppliedSpectroscopy, 1992, 46, 959-965, utilizes partial least-squaresmultivariate calibration models based on whole blood glucose levels.When the models were based on in vitro measurements using whole blood, asubject-dependent concentration bias was retrospectively observed,indicating that additional calibration would be necessary.

In an article by Haaland et al., "Reagentless Near-InfraredDetermination of Glucose in Whole Blood Using Multivariate Calibration,"Applied Spectroscopy, 1992, 46, 1575-1578, the authors suggest the useof derivative spectra for reducing subject-to-subject (or inter-subject)spectral differences. The method was not found to be effective on thedata presented in the paper. First derivatives are an example of ageneral set of processing methods that are commonly used for spectralpretreatrment. A general but incomplete list of these pretreatmentmethods would include trimming, wavelength selection, centering,scaling, normalization, taking first or higher derivatives, smoothing,Fourier transforming, principle component selection, linearization, andtransformation. This general class of processing methods has beenexamined by the inventors and has not been found to effectively reducethe spectral variance to the level desired for clinical predictionresults.

In an article by Lorber et al., "Local Centering in MultivariateCalibration," Journal of Chemometrics, 1996, 10, 215-220, a method oflocal centering the calibration data by using a single spectrum isdescribed. For each unknown sample, the spectrum used for centering thecalibration data set is selected to be that spectrum that is the closestmatch (with respect to Mahalanobis distance) to the spectrum. of theunknown. A separate partial least-squares model is then constructed foreach unknown. The method does not reduce the overall spectroscopicvariation in the calibration data set.

Accordingly, the need exists for a method and apparatus fornon-invasively measuring attributes of biological tissue, such asglucose concentrations in blood, which incorporates a model that issufficiently robust to act as an accurate surrogate for directmeasurement. The model would preferably account for variability bothbetween subjects and within the subject on which the indirectmeasurement is being used as a predictor. In order to be commerciallysuccessful, applicants believe, the model should not require extensivesampling of the specific subject on which the model is to be applied inorder to accurately predict a biological attribute such as glucose.Extensive calibration of each subject is currently being proposed byBioControl Inc. In a recent press release the company defines a 60-daycalibration procedure followed by a 30-day evaluation period.

The present invention addresses these needs as well as other problemsassociated with existing models and calibrations used in methods fornon-invasively measuring an attribute of a biological sample such asglucose concentration in blood. The present invention also offersfurther advantages over the prior art and solves problems associatedtherewith.

SUMMARY OF THE INVENTION

The present invention is a method that reduces the level of interferingspectral variation that a multivariate calibration model needs tocompensate for. An important application of the invention is thenon-invasive measurement of an attribute of a biological sample such asan analyte, particularly glucose, in human tissue. The inventionutilizes spectroscopic techniques in conjunction with improved protocolsand methods for acquiring and processing spectral data. The essence ofthe invention consists of protocols and data-analytic methods thatenable a clear definition of intra-subject spectral effects whilereducing inter-subject spectral effects. Tfte resulting data, which havereduced inter-subject spectroscopic variation, can be utilize in aprediction method that is specific for a given subject or tailored (oradapted) for use on the specific subject. The prediction method uses aminimal set of reference samples from that subject for generation ofvalid prediction results.

A preferred method for non-invasively measuring a tissue attribute, suchas the concentration of glucose in blood, includes first providing anapparatus for measuring infrared absorption by a biological sample suchas an analyte containing tissue. The apparatus preferably includesgenerally three elements, an energy source, a sensor element, and aspectrum analyzer. The sensor element includes an input element and anoutput element. The input element is operatively connected to the energysource by a first means for transmitting infrared energy. The outputelement is operatively connected to the spectrum analyzer by a secondmeans for transmitting infrared energy.

In practicing a preferred method of the present invention, an analytecontaining tissue area is selected as the point of analysis. This areacan include the skin surface on the finger, earlobe, forearm, or anyother skin surface. A preferred sample location is the underside of theforearm. The sensor element, which includes the input element and theoutput element, is then placed in contact with the skin. In this way,the input element and output element are coupled to the analytecontaining tissue or skin surface

In analyzing for a biological attribute, such as the concentration ofglucose in the analyte containing tissue, light energy from the energysource is transmitted via a first means for transmitting infrared energyinto the input element. The light energy is transmitted from the inputelement to the skin surface. Some of the light energy contacting theanalyte-containing sample is differentially absorbed by the variouscomponents and analytes contained therein at various depths within thesample. A quantity of light energy is reflected back to the outputelement. The non-absorbed reflected light energy is then transmitted viathe second means for transmitting infrared energy to the spectrumanalyzer. As detailed below, the spectrum analyzer preferably utilizes acomputer and associated memory to generate a prediction result utilizingthe measured intensities and a calibration model from which amultivariate algorithm is derived.

The viability of the present invention to act as an accurate and robustsurrogate for direct measurement of biological attributes in a samplesuch as glucose in tissue, resides in the ability to generate accuratepredictions of the direct measurement (e.g., glucose level) via theindirect measurements (spectra). Applicants have found that, in the caseof the noninvasive prediction of glucose by spectroscopic means,application of known multivariate techniques to spectral data, will notproduce a predictive model that yields sufficiently accurate predictionsfor future use. In order to obtain useful predictions, the spectralcontribution from the particular analyte or attribute of inte, rest mustbe extracted from a complex and varying background of interferingsignals. The interfering signals vary across and within subjects and canbe broadly partitioned into "intra-subject" and "inter-subject" sources.Some of these interfering signals arise from other substances that varyin concentration. The net effect of the cumulative interfering signalsis such that the application of known multivariate analysis methods doesnot generate prediction results with an accuracy that satisfies clinicalneeds.

The present invention involves a prediction process that reduces theimpact of subject-specific effects on prediction through a tailoringprocess, while concurrently facilitating the modeling of intra-subjecteffects. The tailoring process is used to adapt the model so that itpredicts accurately for a given subject. An essential experimentalobservation is that intra-subject spectral effects are consistent acrosssubjects. Thus, intra-subject spectral variation observed from a set ofsubjects can be used to enhance or strengthen the calibration forsubsequent use on an individual not included in the set. This results ina prediction process that is specific for use on a given subject, butwhere intra-subject information from other subjects is used to enhancethe performance of the monitoring device.

Spectroscopic data that have been acquired and processed in a mannerthat reduces inter-subject spectroscopic variation while maintaininginter-subject variation are herein referred to as generic calibrationdata. These generic data, which comprise a library of intra-subjectvariation, are representative of the likely variation that might beobserved over time for any particular subject. In order to be effective,the intra-subject spectral variation manifested in the genericcalibration data must be representative of future intra-subject spectraleffects such as those effects due to physiological variation, changes inthe instrument status, sampling techniques, and spectroscopic effectsassociated with the analyte of interest. Thus, it is important to use anappropriate experimental protocol to provide representation of theseintra-subject spectral effects.

In each prediction embodiment of the present invention, multivariatetechniques are applied to the generic calibration data to derive asubject-specific predictor of the direct measurement. Each predictionembodiment uses the generic calibration data in some raw or alteredcondition in conjunction with at most a few reference spectra from aspecific subject to achieve a tailored prediction method that is anaccurate predictor of a desired indirect measurement for that particularsubject. Reference spectra are spectroscopic measurements from aspecific subject that are used in the development of a tailoredprediction model. Reference analyte values quantify the concentration ofthe analyte (via direct methods) and can be used in the development of atailored prediction model. Applicants have developed several embodimentsthat incorporate the above concepts.

Each tailored prediction method described herein utilizes genericcalibration data. Generic calibration data can be created by a varietyof data acquisition and processing methods. In a first preferredprocessing method, the generic calibration data are obtained byacquiring a series of indirect measurements from one or more subjectsand a direct measurement for each subject corresponding to each indirectmeasurement. An appropriate experimental protocol is needed to provideadequate representation of intra-subject effects that are expected inthe future (including those associated with the analyte of interest).The mean indirect measurement and the mean direct measurement for eachsubject based on the number of measurements from that subject are thenformed. The indirect measurements are mean centered by subtracting themean indirect measurement of each subject from each of that subject'sindirect measurements. The direct measurements are mean centered bysubtracting the mean direct measurement of each subject from each ofthat subject's direct measurements. That is, the subject-specific meanindirect measurements and subject-specific mean direct measurements actas subject-specific subtrahends. The sets of mean-centered measurements(indirect and direct) comprise the generic calibration data.

There are a number of other related ways for creating genericcalibration data with a subject-specific subtrahend. For example, thesubject-specific subtrahends for the indirect and direct measurementscould be some linear combination of each subject's indirect and directmeasurements, respectively.

In one other specific method for creating generic calibration data, thesubject-specific subtrahends for the indirect and direct measurementsconsist of the mean of the first S indirect measurements of each subjectand the mean of the first S direct measurements of each subject,respectively. Alternately, a moving window reference technique could beutilized wherein the subtrahends are the subject-specific means of the Snearest (in time) indirect and direct measurements, where S is less thanthe total number of reference measurements made on a particular subject.The value of S can be chosen to fit the constraints of the particularapplication, neglecting effects due to random noise and reference error.

In another alternative processing method, the generic calibration datacan be produced in a round-robin reference manner wherein you subtracteach of the patient's reference data from every other referencemeasurement made on that subject in a round-robin fashion.

In a further alternative processing method which is particularly usefulwhen a spectral library associated with a large number of subjectsexists. the generic calibration data are created by subtracting somelinear combination of spectral library data in order to minimizeinter-subject spectral features. Subject-specific attributes can bereduced by subtracting some linear combination of similar spectra. Thatis, the subject-specific subtrahend for a given subject consists of alinear combination of spectra obtained from one or more subjects each ofwhom are different than the given subject. In one embodiment, thespectrum of a given subject would be matched with a combination ofsimilarly appearing spectra from other subjects. In another embodiment,one would match the spectrum of a given subject with a combination ofspectra from other subjects where the matching criteria involvemeasurable parameters such as age, gender, skin thickness, etc.

In a final alternative processing method, the generic calibration dataare created through simulation in a manner that minimizessubject-specific spectral attributes. This methodology requires accuratesimulations of patient spectra, as well as accurate modeling of theoptical system, the sampler-tissue interface, and the tissue opticalproperties which all contribute to such spectral variation. Genericcalibration data can be simulated directly or subject data can besimulated. The simulated subject spectra can subsequently be processedby any of the preceding five processing methods. In an additionalembodiment, the simulated data can be combined with real patient datafor the creation of a hybrid generic calibration data.

Once the generic calibration data have been created, such data is thenutilized to create a tailored prediction process specific for aparticular subject for use in future predictions of the biologicalattribute. The tailored prediction process can be accomplished inseveral ways.

The most straightforward and direct way to tailor the prediction processto a given subject is as follows and will be denoted as directtailoring. First, the generic calibration data are used to develop anintra-subject calibration model for the analyte of interest. This modelherein is referred to as a generic model. By design, the generic modelwill produce predictions that are essentially unaffected byintra-subject spectral variation that is represented in the genericcalibration data and not associated with the analyte of interest. On theother hand, the generic model will produce predictions that areappropriately sensitive to the analyte of interest. The generic model isapplied directly to at least one indirect measurement from a targetsubject for whom there are corresponding direct measurements. Theresulting predictions of the generic model are averaged. The differencebetween the average of the direct measurements and average prediction iscomputed. This subject-specific difference is added to the subsequentpredictions of the generic model as applied directly to the futureindirect measurements from the target subject. The resultant sumscomprise the net predictions of the direct measurement corresponding tothe future indirect measurements from the target subject. It isimportant to note that a single generic model can be used in thetailoring process for a number of target subjects.

A second tailored prediction embodiment uses a combination of at leasttwo subject reference spectra, reference analyte values and the genericcalibration data to create a prediction model that is specific for useon the particular subject. The technique by which the calibration dataand reference spectra are combined uses a linear combination of the datain absorbance units. The combinations of calibration data and referencedata can be done in a structured or random way. It is the applicant'sobservation that random associations work effectively and are easilyimplemented. The process of creating these composite data is referred toas robustification. The resulting calibration spectra contain thereference spectra from the particular patient combined with spectraldata that contains sources of spectroscopic variation associated withphysiological variations, variations associated with samplingtechniques, instrument variation and spectroscopic effects associatedwith the analyte of interest. The composite calibration data can beprocessed to develop a calibration model. The resulting model will bereferred to hereafter as a composite calibration model. The resultingcomposite calibration model is specific for a particular patient and canbe used to generate analyte prediction results for the particularsubject.

In the use of either tailored prediction process, reference spectra andreference analyte values are utilized. The reference information is usedin combination with the generic calibration data to create a tailoredprediction process for use on the particular subject. In general termsthe subject reference information is used to tailor a general processingmethod for use on a particular subject. In an additional embodiment, thesubject reference spectra can be replaced by the use of asubject-matched spectrum or a set of matched spectra. Matched spectraare spectra from another subject or a combined spectrum that interactswith the calibration model in a manner similar to the subject to bepredicted upon. In use, a never-before-seen subject is tested and atleast one spectrum is obtained. The resulting spectrum is used forgenerating a prediction result and as a reference spectrum. In use andin contrast to the two prior embodiments no reference analyte value isused or needed. The implementation of this method requires thefollowing:

1. Identification or creation of a matched spectra through use of thereference spectra.

2. Replacement of the reference spectra with the corresponding matchedspectra.

3. Although reference analyte values are not obtained from thenever-before-seen patient, matched analyte values from the correspondingmatched spectra are used in the processing method in a manner consistentwith the prior uses of reference analyte values.

4. Use of either tailored prediction process.

In practice, the spectral data from the never-before-seen subject iscompared with spectral data that has corresponding biological attributereference values in a spectral library to identify the best method orseveral matched spectra. Matched spectra are spectra from anothersubject that appear similar when processed by the calibration model.Applicants have observed that identical twins are well matched from aspectroscopic model perspective.

As stated previously, the application of known multivariate analysistechniques have not resulted in glucose prediction results at aclinically relevant level. The processing method described overcomesthese known limitations by using a matched spectrum. Thus, the subjecttailoring with this method is accomplished without an actual referenceanalyte value from the individual. The matched spectrum method inconjunction with either tailored prediction process requires a largespectral library to facilitate the appropriate matching between thesubject to be predicted upon and at least one library spectrum. Inimplementation of this matching method, applicants have identifiedmatched spectra by finding those spectra that are most consistent withthe calibration model as reflected by such parameters as Mahalanobisdistance and spectral residual metrics. Other methods of spectral matchwould also have applicability for determination of matched spectra.

These and various other advantages and features of novelty thatcharacterize the present invention are pointed out with particularity inthe claims annexed hereto and forming a part hereof. However, for abetter understanding of the invention, its advantages, and the objectobtained by its use, reference should be made to the drawings which forma further part hereof, and to the accompanying descriptive matter inwhich there are illustrated and described preferred embodiments of thepresent invention.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, in which like reference numerals indicate correspondingparts or elements of preferred embodiments of the present inventionthroughout the several views:

FIG. 1 depicts exemplary spectral variation observed in subjects;

FIG. 2 is a flow chart representing the processing steps associated withgenerating generic calibration data through meancentering;

FIG. 3 is a flow chart representing the steps of the direst tailoringprediction process of the present invention;

FIG. 4 is a flow chart representing the steps of the composite tailoredprediction process of the current invention;

FIG. 5 is a flow chart representing the processing steps associated withgenerating generic calibration data through the fixed reference method;

FIG. 6 is a flow chart representing the processing steps associated withgenerating generic calibration data through the round robin method;

FIG. 7 is a flow chart representing the steps of the composite tailoredprediction process of the current invention;

FIG. 8 is a flow chart representing the steps of the matched spectrummethod in conjunction with the direct-tailored prediction process of thecurrent invention;

FIG. 9 is a flow chart representing the steps of the matched spectrummethod in conjunction with the composite tailored production process ofthe current invention;

FIG. 10 displays the spectrum of generic model coefficients;

FIG. 11 graphically depicts the ability of the present invention topredict glucose using mean centering with direct tailoring for Subject1;

FIG. 12 graphically depicts the ability of the present invention topredict glucose using mean centering with direct tailoring for Subject2;

FIG. 13 graphically depicts the ability of the present invention topredict glucose with the direct tailored prediction process; and

FIG. 14 graphically depicts the ability of the present invention topredict glucose with the composite tailored prediction process.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Detailed descriptions of the preferred embodiments of the presentinvention are disclosed herein. However, it is to be understood that thedisclosed embodiments are merely exemplary of the present invention thatmay be embodied in various systems. Therefore, specific detailsdisclosed herein are not to be interpreted as limiting, but rather as abasis for the claims and as a representative basis for teaching one ofskill in the art to variously practice the invention.

The present invention is directed to a method for non-invasivemeasurement of biological attributes, such as tissue analytes orproperties using spectroscopy. It has been found that the sample is acomplex matrix of materials with differing refractive indices andabsorption properties. Further, because the tissue or blood constituentsof interest are present at very low concentrations, it has been foundnecessary to incorporate a mathematical model derived using multivariateanalysis. However, known methods of applying multivariate analysis tospectral data from a broad range of subjects have failed to produce asufficiently accurate and robust model. To this point, these failuresare largely a consequence of inadequate experimental protocols andinadequate data analytic methods. The present invention solves thesedeficiencies via improvements in experimental protocols and dataanalytic procedures. Experimental protocols have been improved in thesense that the acquisition of a wide variety of intra-subject spectralvariation is emphasized. Coinciding with the improved protocols are dataanalytic methods that modify the calibration data to reducesubject-specific spectral attributes that are unrelated to measuring thebiological attributes of interest. The resulting modified calibrationdata set thus facilitates the development of models that perform well inthe presence of actual within-patient physiological variation. Theprediction methodologies using this core concept are detailed below,subsequent to a description of the method and apparatus used fornon-invasive measurement in conjunction the model.

The present invention utilizes light energy in the near-infrared regionof the optical spectrum as an energy source for analysis. Water is byfail the largest contributor to absorption in tissue in thenear-infrared region because of its concentration, as well as its strongabsorption coefficient. It has been found that the total absorptionspectrum of tissue, therefore, closely resembles the water spectrum.Less than 0.1 percent of the absorption of light is from, for instance,a constituent such as glucose. It has been further found that tissuegreatly scatters light because there are many refractive indexdiscontinuities in a typical tissue sample. Water is perfused throughthe tissue, with a refractive index of 1.33. Cell walls and otherfeatures of tissue have refractive indices closer to 1.5 to 1.6. Theserefractive index discontinuities give rise to scatter. Although theserefractive index discontinuities are frequent, they are also typicallysmall in magnitude and the scatter generally has a strong directionalitytoward the forward direction.

This forward scatter has been described in terms of anisotropy, which isdefined as the cosine of the average scatter angle. Thus, for completebackward scatter, meaning that all scatter events would cause a photonto divert its direction of travel by 180 degrees, the anisotropy factoris -1. Likewise, for complete forward scatter, the anisotropy factor is+1. In the near infrared, tissue has been found to have an anisotropyfactor of around 0.9 to 0.95, which is very forward scattering. Forinstance, an anisotropy factor of 0.9 means that an average photon oflight only scatters through an angle of up to 25 degrees as it passesthrough the sample.

In analyzing for an analyte in tissue, measurements can be made in atleast two different modes. It is recognized that one can measure lighttransmitted through a section of tissue, or one may measure lightreflected or remitted from tissue. It has been recognized thattransmission is the preferred method of analysis in spectroscopy becauseof the forward scattering of light as it passes through the tissue.However, it is difficult to find a part of the body which is opticallythin enough to pass near infrared light through, especially at thelonger wavelengths. Thus, the preferred method for measurement in thepresent invention is to focus on the reflectance of light from thesample. Preferred apparatus and methods for conducting such measurementsare disclosed by Robinson in U.S. Pat. No. 5,830,132, the disclosure ofwhich is incorporated herein by reference.

In preferred embodiments of an apparatus for non-invasively measuring abiological attribute such as a blood analyte concentration, severalelements are combined in conjunction with a mathematical model. Theapparatus generally includes three elements, an energy source, a sensorelement, and a spectrum analyzer. The sensor element preferably includesan input element and an output element, which can include a single lenssystem for both input and output light energy, as for (example a fiberoptic bundle. The input element and output element are in contact with acommon skin surface of an analyte-containing tissue. In an alternativeembodiment, an alternative sensor element arrangement is used, whereinthe input element and output element are arranged on opposing surfacesof an analyte containing tissue. Both embodiments function to give ameasure of the absorption of infrared energy by the analyte-containingtissue. However, the first embodiment is utilized to measure thequantity of light energy that is reflected from the analyte-containingtissue by the analyte components therein. In contrast, the secondembodiment measures the transmission of light energy through theanalyte-containing tissue. In either embodiment, the absorption itvarious wavelengths can be determined by comparison to the intensity ofthe light energy from the energy source.

The energy source is preferably a wide band, infrared black body source.The optical wavelengths emitted from the energy source are preferablybetween 1.0 and 2.5 μm. The energy source is operatively coupled to afirst means for transmitting infrared energy from the energy source tothe input element. In preferred embodiments, this first means can simplyinclude the transmission of light energy to the, input element throughair by placing the energy source proximate the input element or us;e ofa fiber optic cable.

The input element of the sensor element is preferably an optical lens orfiber that focuses the light energy to a high energy density spot.However, it is understood that other beam focusing means may be utilizedin conjunction with the optical lens to alter the area of illumination.For example, a multiple lens system, tapered fibers, or otherconventional optical beam-shaping devices could be utilized to alter theinput light energy.

In both embodiments, an output sensor is utilized to receive reflectedor transmitted light energy from the analyte containing tissue. Asdescribed in conjunction with a method of analysis below, the firstembodiment has an output sensor that receives reflected light energy,while the second embodiment of includes an output sensor which receivestransmitted light through the analyte-containing tissue. As with theinput element, the output element is preferably an optical lens or fiberoptic. Other optical collection means may be incorporated into an outputelement, such as a multiple lens system, tapered fiber, or otherbeam-collection means to assist in directing the light energy to thespectrum analyzer.

A second means for transmitting infrared energy is operatively connectedto the output element. The light transmitted through the second meansfar transmitting infrared energy is transmitted to the spectrumanalyzer. In a preferred embodiment, the operative connection to theoutput element includes transmission of the reflected or transmittedlight energy exiting the output element through a fiber optic or air tothe spectrum analyzer. A mirror or series of mirrors may be utilized todirect this light energy to the spectrum analyzer. In a preferredembodiment, a specular control device is incorporated to separate thespecular reflected light from diffusely reflected light. This device isdisclosed in co-pending and commonly assigned application Ser. No.08/513,094, filed Aug. 9, 1995, and entitled "Improved DiffuseReflectance Monitoring Apparatus," now U.S. Pat. no. 5,636, 633, issuedJun. 10, 1997, the disclosure of which is incorporated herein byreference.

In practicing a preferred method of the present invention, ananalyte-containing tissue area is selected as the point of analysis. Apreferred sample location is the underside of the forearm. The sensorelement, which includes the input element and the output element, isthen placed in contact with the sample area.

In analyzing for a biological attribute, such as for the concentrationof glucose in the analyte-containing tissue, light energy from theenergy source is transmitted through the first means for transmittinginfrared energy into the input element. The light energy is transmittedfrom the input element to the skin surface. The light energy contactingthe skin surface is differentially absorbed by the various componentsand analytes contained below the skin surface within the body (i.e.,blood within vessels) therein. In a preferred embodiment, thenon-absorbed light energy is reflected back to the output element. Thenon-absorbed light energy is transmitted via the second means fortransmitting infrared energy to the spectrum analyzer.

In a preferred embodiment, a biological attribute, such as theconcentration of glucose in the tissue, is determined by first measuringthe light intensity received by the output sensor. These measuredintensities in combination with a calibration model are utilized by amultivariate algorithm to predict the glucose concentration in thetissue. In preferred embodiments, the calibration model empiricallyrelates the known biological attribute in the calibration samples to themeasured intensity variations obtained from the calibration samples. Thespectrum analyzer of the present invention preferably includes afrequency dispersion device and photodiode array detectors inconjunction with a computer to apply the data received from such devicesto the model stored therein to predict the biological attribute ofinterest of the subject.

As previously stated, the computer includes a memory having storedtherein a multivariate calibration model empirically relating knownbiological attributes, such as glucose concentration, in a set ofcalibration samples to the measured intensity variations from thecalibration samples, at several wavelengths. The present inventionincludes prediction methodologies with sufficient accuracy to act as asurrogate predictor of biological attributes so that direct measurementscan be dramatically reduced or eliminated.

Generally, the method of the present invention incorporates genericcalibration data in combination with subject-specific data to create atailored prediction process. The resulting subject-tailored predictionprocess combines selected portions of multiple subject spectralvariances and subject reference spectra. The tailored prediction processis made subject specific by incorporating a minor amount ofsubject-specific spectral data and does not require extensivecalibration testing of the individual subject on which the model is tobe applied. The various embodiments described below require datacollection and processing to be applied in both a calibration and aprediction phase.

In the calibration phase, the methods generally require the realizationof calibration data that has been modified in such a way as to reduce oreliminate subject-specific spectral attributes that are unrelated to thebiological attribute of interest in the test. The resulting modifiedcalibration data has reduced inter-subject spectroscopic variation whilemaintaining other relevant sources of spectroscopic variation. Otherknown sources of spectroscopic variation include within subjectphysiological variation, variation associated with sampling errors,instrument variation, and spectroscopic effects associated with theanalyte or attribute of interest. Such calibration data is referred toherein as generic calibration data.

In the prediction phase, two general embodiments are incorporated. Thefirst method focuses on developing a model from the generic calibrationdata followed by introducing subject-specific data from a particularindividual, whose attributes are to be predicted, and utilizing thisinformation to create a subject specific prediction through use of thegeneric model. The second general approach includes incorporatingsubject-specific data from an individual subject to be tested along withthe generic calibration data. The resulting composite data is used inthe multivariate analysis to generate a prediction function. Theresulting prediction function resulting from the combination of genericcalibration data and subject-specific data is a composite calibrationmodel that is subject specific.

In all embodiments, a model is developed using spectroscopic variationfrom multiple subjects wherein the tailored prediction method uses oneor more reference spectroscopic measurements from a specific patient sothat the prediction process becomes subject tailored for that specificsubject. Applicants have found that the model is an accurate predictorbecause it incorporates the physiological variation from other subjectsto enhance or strengthen a calibration for subsequent use on a givenindividual. The prediction procedure results in a method that isspecific for use on a given subject, but where information not from thesubject is used to enhance prediction accuracy, in combination withspectral information from that particular individual.

In practicing the present invention, the first step of one preferredmethod is to generate generic calibration data that is essentially freefrom subject-specific effects. This step may be accomplished byutilizing a device such as disclosed in the aforementioned Robinson U.S.Pat. No. 4,975,581 to indirectly measure from one to many subjects, eachat a variety of physiological (such as taking glucose measurement over aperiod of time) and spatial (such as taking glucose measurements from avariety of locations on the body) states.

A preferred method to generate generic calibration data is referred toas meancentering and is depicted in the flow chart of FIG. 2. Here, letY_(ijk) be the spectral measurement (e.g., log(intensity)) of the k^(th)wavelength within the j^(th) spectrum from the i^(th) subject.Subject-specific effects are removed as follows. First, form the meanspectrum for each subject. The mean spectrum at the k^(th) wavelengthfor the i^(th) subject is: ##EQU1## where J_(i) is the number of spectrafrom the i^(th) subject. The appropriate mean spectrum is then removedfrom each observed spectrum: Y_(ijk) =Y_(ijk) -M_(ik). This process maybe referred to as meancentering the spectra by subject.

Associated with each spectrum, we also have a direct measurement ofreference blood-glucose concentration, G_(ij). The glucoseconcentrations are also meancentered by subject, resulting in g_(ij)=G_(ij) -N_(i), where Ni is the mean glucose concentration for thei^(th) subject and defined as: ##EQU2##

The meancentered glucose values may be scaled by a subject-specificfactor (k) that is equal to the relative magnitude of the spectraleffect of 1 mg/dL of in vivo blood-glucose for that subject. Thisscaling serves to normalize glucose signals across subjects that couldbe different across subject (e.g., due to pathlength differences) to astandard in vivo glucose signal. The particular example of meancenteredprocessing is cited to illustrate a specific processing embodiment ofthe invention. It is recognized that the use of this invention mayinvolve generation of generic calibration (late through multipleprocessing means. Subject-specific spectroscopic variances can bereduced by subtracting (in absorbance units, or performing a similaroperation in any other data space) some linear combination of eachsubject's reference spectra and reference analyte values. At this point,the meancentered spectra and meancentered, (and possibly scaled) glucoseconcentrations are used in the multivariate calibration modeldevelopment.

Once the generic calibration data has been created, such data are thenutilized in forming a tailored prediction process for a particularsubject for use in future predictions of the biological attribute. Thiscan be accomplished in several ways such as use of a direct-tailoringtechnique or alternatively a composite technique. Common to both methodsis a calibration model. A representation of a linear multivariatecalibration model (a specific type of calibration model) is G=b₀ +b₁ ·y₁+b₂ ·y₂ +. . . +b_(q) ·Y_(q), where the b_(k) 's are model parameters.Development of G from the meancentered indirect data Y_(ijk) or othergeneric calibration data and the direct data g_(ij) is a routine matterfor one skilled in chemometrics, as taught by H. Martens et al.,Multivariate Calibration, (1989), John Wiley, Chichester.

Note that the use of generic calibration data for developing the genericmodel in this embodiment is believed important for preserving sufficientsensitivity to detect outlier (or anomalous) spectra during prediction.Without the meancentering operation of the invention on the spectra,Mahalanobis-distance and other outlier detection metrics are likely tobe based heavily on ancillary inter-subject effects aid, therefore, notbe sufficiently responsive to unusual intra-subject effects.

Once the generic model is in hand, it must be tailored (or adapted) fora specific subject. Two direct tailoring versions of this procedure aredescribed for the present embodiment. In the first version it is assumedthat the scale factor, k, pertaining to the relative magnitude of thespectral effect of 1 mg/dL of in vivo blood-glucose is known withadequate precision. In the second version it is assumed that this scalefactor is unknown and must be estimated.

Version 1 (k known)

1. Make one (or several) spectral measurement of the target subject'stissue (perhaps varying the spatial position when multiple measurementsare obtained at about the same time). Denote the resultant spectrum (oraverage spectrum when multiple spectra are obtained) by Y_(ref), whereY_(ref) ={y_(r1), y_(r2), . . . , y_(rq) }. The idea is to obtain veryprecise spectral measurements for the adaptation process.

2. As close as possible in time with respect to the collection of thespectrum (spectra), an accurate reference measurement of in vivoglucose, G_(ref), is obtained from the subject (e.g., blood draw).

3. Use the generic model in conjunction with Y_(ref) to obtain a rawprediction of glucose, P₀, that will be used as the basis to adapt thegeneric model to the subject. Once steps 1-3 have been completed,non-invasive measurements of glucose can be determined in the future asfollows.

4. Obtain a new spectral measurement of the subject's tissue,

    Y.sub.new ={y.sub.n1, y.sub.n2, . . . , y.sub.nq }.

5. Apply the generic model to Y_(new) to obtain an unadapted prediction,P_(new).

The prediction of glucose (adapted to that subject) is ##EQU3##

Version 2 (k unknown)

In this format, steps 1-3 (from version 1) are performed at least twice(once when the target subject is experiencing a relatively low in vivoglucose level, the other when the target subject is experiencing arelatively high in vivo glucose level). At the relatively low glucoselevel, we obtain:

    Y.sub.new.sup.lo ={y.sub.n1.sup.lo, y.sub.n2.sup.lo, y.sub.n3.sup.lo, . . . }

At the relatively high glucose level, we obtain:

    Y.sub.new.sup.hi ={y.sub.n1.sup.hi, y.sub.n2.sup.hi, y.sub.n3.sup.hi, . . . }

As in version 1, apply the generic model to Y_(new) to obtain anuncorrected prediction, P_(new). The prediction of glucose (adapted tothat subject) is: ##EQU4## Note that it is straightforward (and perhapsdesirable) to modify this technique to include more than one or tworeference samples per target subject.

In summary, the proposed prediction method of this first embodimentprovides a solution to the difficulties associated with building auniversal calibration model that needs to be appropriately responsive tosubject-to-subject special variation as well as spectral variationwithin subjects over time and space. The proposed method is illustratedin the flow chart of FIG. 3 and provides a simple subject-specificadaptation to a generic model that is appropriately sensitive to thespectral variation within a subject. Development of this type ofsubject-specific model is a substantial improvement (with respect toefficiency) when compared to the development of subject-specific modelsvia intensive optical sampling of each individual subject.

The second prediction technique of the present invention is thecomposite technique that is depicted in the flow chart of FIG. 4. Withthe composite technique, two or more reference measurements, whichinclude both the spectra and the analyte reference values, are made onthe particular subject and these data are added in a random fashion tothe generic calibration data. This process is represented by theequations:

    y.sub.ijk =y.sub.ijk +y.sub.ilk.sup.ref, g.sub.ij =g.sub.ij +g.sub.ilk.sup.ref,

where y_(ilk) ^(ref) irk is the k^(th) element of the l^(th) referencespectrum for subject i, g_(il) ^(ref) is the l^(th) glucose referencevalue for subject i, and a random value of l is chosen for each i, jpair

The resulting composite data is then used in conjunction with amultivariate analysis technique to generate a calibration model which issubject tailored due to the addition of reference spectral measurementsand reference analyte measurements prior to generating the model. Theresulting subject-tailored model is then applied to other spectra fromthe same subject on whom the reference measurements were made,Predictions are made with the resulting calibration model by followingstandard chemometric practices known to one skilled in the art.

Generic calibration data can also be created by a fixed referencetechnique. The fixed reference technique is depicted in the flow chartof FIG. 5. This technique can be utilized to modify the calibration databy subtracting the mean of the first S calibration spectra and referencevalues from a particular subject from each of the subject's referencemeasurements, where S is less than the total number of referencemeasurements made on a particular subject. This is represented by theequations: ##EQU5## In the alternative, a moving window referencetechnique may be utilized wherein you subtract the mean of the S nearest(in time) calibration spectra and reference values from each of thesubject's calibration measurements, where S is less than the totalnumber of reference measurements made on a particular subject. Thismethod is represented by the equations: ##EQU6## The value of S can bechosen to fit the constraints of the particular application, neglectingeffects due to random noise and reference error.

Alternatively, the generic calibration data may be generated in around-robin reference manner wherein you subtract each of the patient'sreference data from every other reference measurement made on thatsubject in a round-robin fashion. The round-robin method is depicted inthe flow chart of FIG. 6. This method is represented by the equations:##EQU7##

A final method used for generating generic calibration data isparticularly useful where a large spectral library, including spectraand reference values from multiple people exists. The library data aremodified to reduce or eliminate subject-specific spectral attributes bysubtracting some linear combination of spectral library data in order tominimize cross-subject spectral features. The methods of this embodimentare depicted in the flow chart of FIG. 7. Thus in modifying the spectrallibrary data, to create generic calibration data, a given subject'sspectra are modified through the use of a similar patient spectra.Similar patient spectra are those spectra that when subtracted from aspecific subject results in a spectral difference that is less than theaverage difference across all subjects. The similar spectrum can be fromanother subject or can be formed by combining several subjects to createa similar spectrum.

In an additional embodiment, patient spectra are created throughsimulation in a manner that minimizes subject-specific spectralattributes. This methodology requires accurate simulations of patientspectra, which would include high accurate modeling of the opticalsystem, the sampler-tissue interface, and the tissue optical propertieswhich all contribute to such spectral variation. Such simulated data canbe generated and removed from measured calibrated data to reducepatient-specific characteristics. The modified calibration model datacan then be utilized in conjunction with data from a specific patient totailor the model for use in predicting biological attributes of thatpatient with the above methods.

Once the generic calibration data has been created, such data is thenutilized in forming a tailored prediction process for a particularsubject for use in future predictions of the biological attribute. Thiscan be accomplished in several ways, such as use of the direct-tailoredtechnique, or alternatively, the composite technique previouslydescribed

With either the direct-tailored prediction method or the compositetailored prediction method as previously described, the referencespectra can be replaced by a matched spectra. The flow charts of FIGS. 8and 9 depict matched spectra methods with bidirects tailored predictionand composite tailored prediction, respectively. With this method, anever-before-seen subject is then tested and at least one targetspectrum or set of spectral data is acquired. However, no analyte ordirect measurement is required from the patient. Rather, the spectraldata from the never-before-seen patient is compared with spectral datawhich has corresponding biological attribute reference values in aspectral library to identify the best reference spectrum or spectra thatcorresponds to the target spectrum of the never-before-seen patient.This reference spectrum can be compared with the target spectrum todetermine the level of match. Thus, the subject tailoring with thismethod is accomplished without an actual reference analyte value. Thismethod relies on a large spectral library to Facilitate the appropriatematching between a target spectrum and a single spectral library entryor several library entries.

In the direct-tailored prediction method the matched spectrum andcorresponding reference analyte values are used instead of actualreference spectra and analyte values from the subject to be predictedupon. The following equation, define the substitution and predictionsteps:

    G.sub.new =P.sub.new -P.sub.0.sup.SIM +G.sub.ref.sup.SIM

where

P_(new) is the raw prediction of the new spectrum Y_(new) using thegeneric model,

P₀ ^(SIM) is the raw prediction of the similar spectrum Y^(SIM)identified in the spectral library,

G_(ref) ^(SIM) is the referenced valve associated with the similarspectrum identified in the spectral library

One requirement of this methodology is the ability to find anappropriate match within the spectral library. If no single subject isan appropriate match, a matched spectrum can be created by combiningspectra from other patients. In practice the matched spectrum, acombination of spectra and inference values from subject in the spectrallibrary, is created through a weighted linear combination of absorbancespectra. The various coefficients applied to the individual libraryspectra wan be adjusted such that the best possible match is obtained.The matched spectrum created through other subject combinations iscreated by the following equations: ##EQU8## where Y_(JK) ^(SIM) is theK^(th) element of the J^(th) spectrum selected from the spectrallibrary, G_(j) is the corresponding reference value, and thecoefficients, c, are chosen to optimize the spectral similarity withY_(new)

The resulting matched spectrum and reference value is used in a mannerconsistent with a matched spectrum obtained from a single patient.

In using the composite tailored prediction process generic calibrationdata is combined with one or more reference spectra and reference vallesto create a data set that is subsequently used for generation of acalibration model. The reference spectra used for the composite tailoredprocess can be replaced by matched spectra. In practice a fixed numberof best-matched spectra from the subject library can be used asreference spectra. In an alternative method any spectra which meet apredetermined level of matching could be used as reference spectra. Inpractice, the level of match ha, , been determined by first calculatingthe difference between the target spectrum and the possible matchedspectrum. The resulting difference spectrum is then used in conjunctionwith the calibration model to determine such parameters as theMahalanobis distance and spectral residual metrics.

Once appropriate matched spectra are determined these spectra are usedin a manner consistent with the composite tailored prediction methodusing reference spectra from the actual subject to be predicted upon.

In addition to the above benefits, application of the methods disclosedherein, such as monitoring blood/glucose levels non-invasively in thehome where a single instrument unit (e.g., spectrometer) is paired witha single subject, provides some substantial benefits with respect tocalibration transfer and maintenance. Calibration transfer refers to theprocess of migrating a master calibration model to a specific unit. Dueto manufacturing variation across units, each unit will differ in subtleways such that the same object will appear slightly different acrossunits (e.g., resulting in slightly different spectra in the case ofspectroscopy). Calibration maintenance refers to the process ofmaintaining a functional model across different instrument states (e.g.,induced by changing a discrete component). The generic subject model(which is based on data that has within subject variation removed) is infact a generic instrument/subject model. That is, the specific effect ofthe instrument has also been removed through the process used to modifythe data set. Preferably, a generic instruments object model isdeveloped by combining data across units and subjects within a unit. Ineither case (using a single unit or multiple units for developing ageneric model), one can see that the series of measurements that aretaken to adapt to the subject simultaneously and implicitly provideadaptation to the specific instrument and current instrument state.Thus, this single generic model is adaptable to an arbitrary subjectbeing measured on an arbitrary unit from an entire production run ofinstruments. Furthermore, this method will facilitate the detection ofanomalous conditions with respect to the subject and instrument duringprediction.

EXAMPLES OF METHOD

A number of clinical studies have recently been performed to assess theperformance of some of the subject tailored prediction methods disclosedin this application. In one such study, generic calibration data wereobtained from 18 diabetic subjects who were repeatedly measured over aspan of 7 week. The intent of observing the subjects for such a longperiod of time was to develop calibration data that spanned significantlevels of natural intra-subject physiological variation (including butnot limited to glucose variation) and sampling variation. In addition,the study protocol involved the deliberate perturbation of thespectrometer and its local environment to induceinstrumental/environmental effects into the generic calibration data.These perturbations were carefully selected to span the expectedlong-term operating conditions of the instrument. Activities, such asthese, are extremely important for developing generic calibration datathat will facilitate valid predictions into the future.

Spectral and reference data were acquired twice per week from mostsubjects. A few subjects were unable to keep all of their appointmentsto provide spectral and reference data. During each appointment, 5separate spectral measurements at different spatial positions on theunderside of the forearm were acquired over a 15-minute period usingreflectance sampling from 4200-7200 wavenumbers (390 discretewavelengths were involved). In addition, two capillary glucose referencemeasurements were obtained via blood draws from each subject during eachdata acquisition period. The blood draws were performed immediatelybefore and after th(e acquisition of the spectral data. Time-basedinterpolation was used to assign an appropriate capillary glucosereference value to each spectrum. A total of 1161 spectra (some acquiredspectra were deemed outliers and were discarded) and associatedreference glucose values comprise the calibration data.

The spectral and capillary glucose reference data were mean-centered bysubject to form the generic calibration data. A generic calibrationmodel was fit to the calibration data using principal componentsregression without an intercept. Due to the nature of the genericcalibration data (the mean-centered spectra and reference values havemean zero), the intercept is not needed. In terms of the spectral datathis model is of the form, G=b₁ ·y₁ +b₂ ·y₂ +. . . +b_(q) ·y_(q). Themodel coefficients, (b₁, b₁, . . . , b_(q)), are shown in FIG. 10. Thismodel is clearly sensitive to glucose since glucose has absorption bandsat 4300 and 4400.

In order to test the efficacy of the subject tailored predictionmethods, the generic model was tailored (via direct tailoring) to twoadditional diabetic subjects who are distinct from the 18 subjects whosedata were used to develop the generic calibration data/model. The periodof observation for these two additional subjects spanned more than sixmonths, beginning with the initial measurements of the original 18subjects. Thus, the two additional subjects were observed for more thanfour months following the acquisition of the generic calibration data.As in the case of acquiring the calibration data, 5 separate spectralmeasurements at different spatial positions on the underside of theforearm were acquired over a 15-minute period during each dataacquisition period. In addition, capillary glucose referencemeasurements were acquired from each of the two subjects during eachdata acquisition period according to the protocol described earlier.

During the first 7 weeks of observation and coinciding with themeasurements of the original 18 subjects, the two additional subjectswere observed twice per week (with one exception). The additionalmeasurements were made were roughly 2 and 4 months beyond the initial7-week period. The spectra and reference values obtained during thefirst data acquisition period were used to tailor the generic model toeach subject. These tailored models were used to predict the glucoselevels associated with subsequently obtained spectra. FIGS. 11 and 12compare these predictions (averaged within a data acquisition period)with the reference measurements (also averaged within a data acquisitionperiod) for each subject. The bottom half of each figure allows for adirect comparison of predicted glucose with the reference glucose. Thetop half of each figure provides a visualization of predictionperformance versus time. The following conventions are used in bothfigures. The solid lines connect the reference glucose values over theentire measurement period. The `x` symbols d(note the predictions duringthe tailoring period (by definition the average prediction is identicalto the average reference in this case). The `*` symbols denotepredictions during the remainder of the initial 7-week period. Note thatthese predictions are truly prospective with respect to the uniquespectral changes induced by each subject following the tailoring period.The `o` symbols denote predictions made after the initial 7-week period.These predictions are truly prospective with respect to the uniquespectral changes induced by each subject and the instrument/environmentfollowing the tailoring period. From these figures it is clear thanclinically useful predictions of blood-glucose can be made using theproposed method.

It is interesting to note that there is no apparent degradation inprediction performance with respect to the first subject over the6-month period of observation following tailoring (see FIG. 11). Incontrast with respect to the, second subject (see FIG. 12), predictionperformance worsened over time. In this case, the tailored modelconsistently underpredicted glucose (by about 40 mg/dL) over the lastseveral data acquisition periods (perhaps due to some unmodeledphysiological effect). One way to remedy these systematic predictionerrors would be to re-tailor (or re-adapt) the generic model to asubject on a regular basis. If needed, re-tailoring on a weekly basiswould seem to be only a minor inconvenience for users of thistechnology.

Additional tests have also been performed that enabled the subjecttailored prediction methods to be tested. The test data used spectralmeasurements obtained from 20 subjects over a total span of 16 weeks.The protocol for the study required that each subject have spectralmeasurements taken on 2 or 3 separate days per week for 8 weeks,spanning the 16-week study duration. Each time a subject came in for astudy "sitting," 4 separate spectral measurements at different spatialpositions on the underside of the forearm were acquired over a 15-minuteperiod, as well as two capillary glucose reference measurements, whichbounded the spectral collection. A total of 1248 spectra (reflectancesampling from 4200-7200 wavenumbers [390 discrete wavelengths]) andassociated reference glucose values were used to develop the calibrationdata. The resulting data set was processed through the mean centeringmethod and generic calibration data were obtained. To adequately testthe true prediction capabilities of the methods, the subject to beevaluated was excluded from the data used to develop the genericcalibration data. The exclusion of one patient from the calibration datawith subsequent evaluation of their performance is commonly referred toas patient-out cross-validation. The cross-validated generic calibrationdata was adapted for each of the 16 diabetic subjects (4 subjects werenot present for the entire study) and resulted in predictions the finaltwo days of that subject's data. Adaptation to each subject wasperformed using data from 5 separate sittings of the subject, 4 sittingswere from the first two weeks of data collection and the fifth sittingwas from a day that was two days prior to the first validation day. Thesecond validation day occurred two days after the first. FIGS. 13 and 14provide the prospective (in time) prediction results associated with thesubjects. The figures show the predicted glucose values for the twovalidation days relative to the corresponding glucose reference valuesobtained by capillary draw for all 16 subjects measured. FIG. 13 showsthe results using the direct-tailored method discussed in the body ofthis disclosure. FIG. 14 shows the results using the composite-tailoredmethod, also discussed earlier in this disclosure. From these figures itis clear than clinically useful predictions of blood-glucose can be madeusing the proposed method.

The particular examples discussed above are cited merely to illustrateparticular embodiments of this invention. It is contemplated that theuse of the invention may involve methods for multivariate calibrationand prediction and their application to the non-invasive ornon-destructive spectroscopic measurement of selected variables in anenvironment. Although blood glucose (the variable) and people (theenvironment) are the focus of this disclosure, calibration of othervariables such as blood alcohol levels, and other subjects, such asscans of a physical scene from which information about the scene isdetermined, is contemplated. For example, an airborne scan of a site(geophysical environment) might provide information whereby multivariateanalysis of spectra could determine the amount of pollutants (thevariables) at the site (the environment), if the scanning device hadbeen calibrated for pollutants. In this case, prediction of pollutantlevels would be the tailored to a particular site. In another example,one might be interested in predicting the level of a certain chemicalspecies (the variable) in a chemical reactor (the environment) usingspectral methods. If the intra-reactor spectral variability wereconsistent across different reactors, then generic calibration datacould be obtained by using reactor-specific subtrahends. Predictionscould be tailored to each reactor.

In addition, while the invention is disclosed as a method of calibratinga single measurement device, it is also contemplated that themeancentered data could be obtained form a number of units that measureboth the same subjects and different subjects. Lastly, the genericcalibration discussed above preferably uses more than one subjectbecause multiple subjects permit a sufficient quantity of intra-subjectvariation data to be obtained in a short period of time. However, forother situations where there are not multiple subjects, such as theobservation of a unique chemical process, the calibration data may beobtained from the one site over an extended period of time. It isintended that the scope of the invention be defined by the claimsappended hereto.

New characteristics and advantages of the invention covered by thisdocument have been set forth in the foregoing description. It will beunderstood, however, that this disclosure is, in many respects, onlyillustrative. Changes may be made in details, particularly in matters ofshape, size, and arrangement of parts, without exceeding the scope ofthe invention. The scope of the invention is, of course, defined in thelanguage in which the appended claims are expressed.

What is claimed is:
 1. A method for generating a prediction result foruse on a specific subject to predict a biological attribute of thatsubject using spectroscopy as a surrogate indirect measurement for adirect measurement of said biological attribute, said method comprisingthe steps of:(a) acquiring a calibration data set and modifying saidcalibration data set in a manner that reduces the spectral variation dueto subject specific attributes; (b) generating a model by applyingmultivariate analysis to said. modified calibration data set; and (c)using a prediction process to predict an unknown amount of saidbiological attribute in a target spectroscopic measurement from saidspecific subject, said prediction process utilizing said model inconjunction with one or more reference measurements.
 2. The method ofclaim 1, wherein said reference measurements are spectroscopicmeasurements.
 3. The method of claim 1, wherein said referencemeasurements include both spectroscopic measurements and directmeasurements from said specific subject.
 4. The method of claim 3,wherein said direct measurements are a blood analyte measurement.
 5. Themethod of claim 1, wherein said calibration data is obtained from aseries of spectroscopic measurements for a number of subjects withcorresponding direct measurements of said biological attribute.
 6. Themethod of claim 5, wherein modifying said calibration data set to reducesubject specific spectral attributes in said calibration data setincludes forming the mean indirect measurement and mean directmeasurement for each subject from which calibration data is obtainedbased on the number of measurements from that subject followed by meancentering the indirect measurement by subject by subtracting the meanindirect measurement from each subject from each indirect measurement,and meancentering the direct measurement by subtracting the mean directmeasurement from each direct measurement for each subject.
 7. The methodof claim 5, wherein modifying said calibration data set to reducesubject specific spectral attributes for each subject in saidcalibration data set includes subtracting the mean of the first Sindirect measurements and direct measurements from a particular subjectfrom each of the subject's indirect measurements, where S is less thanthe total number of indirect and direct measurements made on thatsubject in generating the calibration data set.
 8. The method of claim5, wherein modifying said calibration data set to reduce subjectspecific spectral attributes for each subject in said calibration dataset includes subtracting the mean of the S nearest in time indirect anddirect measurements of a subject from each of the subject's indirect anddirect measurements, where S is less than the total number of indirectmeasurements used in forming the calibration data set.
 9. The method ofclaim 5, wherein modifying said calibration data set to reduce subjectspecific spectral attributes for each subject in said calibration dataset includes subtracting each of a subjects direct and indirectmeasurements from every other direct and indirect measurement made onthat subject in a round-robin fashion.
 10. The method of claim 5,further including providing a stored spectral library wherein modifyingsaid calibration data set to reduce subject specific spectral attributesfor each subject in said calibration data set includes subtracting acombination of spectral data from a stored spectral library based onmatching said series of spectroscopic measurements from each of saidnumber of subjects with a stored measurement in said spectral library.11. The method of claim 5, wherein modifying said calibration data setto reduce subject specific spectral attributes for each subject in saidcalibration data set includes subtracting simulated data from indirectmeasurements, said simulated data derived from prior modeling ofspectral attributes.
 12. The method of claim 3, wherein said predictionprocess utilizes said model and said references measurements tocalculate a prediction of the direct measurement and utilizes thedifference between the prediction of the direct measurement and thedirect measurement of said biological attribute to estimate a correctionfactor.
 13. The method of claim 1, wherein said reference measurementsare replaced by matched measurements.
 14. The method of claim 13,wherein said matched measurements are selected from said spectrallibrary by calculating a measure of the difference between said targetspectroscopic measurement and said library spectra.
 15. A method ofgenerating a calibration model that is essentially free from subjectspecific effects comprising building a generic model by:(a) obtaining aseries of indirect measurements from a number of subjects, and obtaininga direct measurement for each subject corresponding to each indirectmeasurement; (b) forming the mean indirect measurement and the meandirect measurement for each subject based on the number of measurementsfrom said each subject; (c) meancentering the series of indirectmeasurements by subject by subtracting the mean indirect measurementfrom each subject from each indirect: measurement, and meancentering thedirect measurement by subtracting the mean direct measurement from eachdirect measurement for each subject; and (d) forming a genericcalibration model from the meancentered direct and indirectmeasurements.
 16. The method if claim 15, further comprising tailoringthe generic calibration model to a specific subject.
 17. The method ofclaim 15, wherein said indirect measurements are spectral measurements.18. The method of claim 15, wherein the measurements are made on asingle measurement device, whereby the generic calibration model is tobe utilized in said single measurement device.
 19. The method of claim15, wherein said direct measurements are of a desired blood componentand said desired blood component is measured by invasively removingblood from said each subject and analyzing the blood for said desiredblood component.
 20. The method of claim 19, wherein the desiredcomponent is glucose.
 21. The method of claim 16, wherein the tailoringstep further comprises:(a) making a direct measurement, G_(ref), and atleast one indirect measurement Y_(ref), of the specific subject; (b)using the generic calibration model with Y_(ref) to obtain a rawprediction, P_(o), of the physical characteristic.
 22. The method ofclaim 21, wherein the measurements are made on a single measurementdevice, whereby the generic calibration model is to be utilized in saidsingle measurement device.
 23. The method of claim 21, furthercomprising: making a plurality of indirect measurements of the specificsubject, Y_(new) ;using the generic calibration model with Y_(new) toobtain an untailored prediction, P_(new) ; and predicting the physicalcharacteristic G_(new) for the subject as a function of P_(new), P_(o),and G_(ref).
 24. The method of claim 23, wherein G_(new) is also afunction of a known scale factor.
 25. The method of claim 21, whereinthe tailoring step further comprises:determing P₀ and G_(ref) accordingto the method of claim 22 once with the specific subject at a relativelyhigh level of the physical characteristic and once with the specificsubject at a relatively low level of the physical characteristic; anddetermining a scale factor based on P₀ and G_(ref) at high and lowlevels.
 26. A method for generating a prediction result for use on aspecific subject to predict a biological attribute of said specificsubject using spectroscopy as a surrogate indirect measurement for adirect measurement of said biological attribute, said method comprisingthe steps of:(a) using a modified calibration data set previouslyprocessed in a manner that reduces the spectral variation due to subjectspecific attributes; (b) generating a calibration model throughapplication of a multivariate algorithm that uses a compositecalibration data set formed by combining the modified calibration datawith two or more reference measurements; and (c) predicting an unknownamount of said biological attribute in a target spectroscopicmeasurement utilizing said calibration model.
 27. The method of claim26, wherein said composite calibration data is created by combining in alinear manner reference measurements with said calibration data, thecombining process to include both reference spectra and referenceanalyte measurements.
 28. The method of claim 26, wherein saidcalibration data is obtained from a series of spectroscopic measurementsfor a number of subjects with corresponding direct measurements of saidbiological attribute.
 29. The method of claim 28, wherein modifying saidcalibration data set to reduce subject specific spectral attributes foreach subject in said calibration data set includes forming the meanindirect measurement and mean direct measurement for each subject basedon the number of measurements from that subject followed by mean themean centering the indirect measurement by subject by subtracting themean indirect measurement from each subject from each indirectmeasurement, and meancentering the direct measurement by subtracting themean direct measurement from each direct measurements for each subject.30. The method of claim 28, wherein said calibration data with reducedsubject specific spectral attributes for each subject is modified bysubtracting the mean of the first S indirect measurements and directmeasurements from a particular cane of said number of subjects from eachof the subject's indirect measurements, where S is less than the totalnumber of indirect and direct measurements made on said particular oneof said number of subjects in generating the calibration data set. 31.The method of claim 28, wherein said calibration data with reducedsubject specific spectral attributes for each subject is modified bysubtracting the mean of the S nearest in time indirect and directmeasurements of a subject from each of the subject's indirect and directmeasurements, where S is less than the total number of indirectmeasurements used in forming the calibration data set.
 32. The method ofclaim 28, wherein said calibration data with reduced subject specificspectral attributes for each subject is modified by subtracting each ofa subjects direct and indirect measurements from every other direct andindirect measurement made on that subject on a round-robin fashion. 33.The method of claim 28, further including providing a stored spectrallibrary wherein said calibration data with reduced subject specificspectral attributes for each subject is modified by subtracting acombination of spectral data from a stored spectral library based onmatching said series of spectroscopic measurements from each of saidnumber of subjects with a stored measurement in said spectral library.34. The method of claim 28, wherein modifying said calibration data setto reducee subject specific spectral attributes for each subject in saidcalibration data set includes subtracting simulated data from indirectmeasurements, said simulated data derived from prior modeling ofspectral attributes.
 35. The method of claim 26, wherein said referencemeasurements are replaced by matched measurements.
 36. The method ofclaim 35, wherein said matched measurements are selected from saidspectral library by calculating a measure of the difference between saidtarget spectroscopic measurement and said library spectra.
 37. A methodfor predicting a measure of a biological attribute for a specificsubject, comprising:(a) obtaining a calibration data set of direct andindirect measurements of the biological attribute from a plurality ofcalibration subjects, wherein the calibration data set has been modifiedto reduce variations therein due to subject specific attributes for eachcalibration subject; (b) developing a subject-specific calibration modelfrom said modified calibration data set tailored for the specificsubject with at least one reference measurement of the biologicalattribute from the specific subject; (c) obtaining at least one indirectmeasurement of the biological attribute for the specific subject; and(d) using the said subject-specific calibration model and said "at leastone" indirect measurement of the biological attribute for the specificsubject to predict a measure of the biological attribute in the specificsubject.
 38. The method of claim 37, wherein modifying said calibrationdata set to reduce subject specific spectral attributes for eachcalibration subject in said calibration data set includes subtractingsimulated data from indirect measurements, said simulated data derivedfrom prior modeling of spectral attributes.
 39. The method of claim 37,further including providing a stored spectral library wherein saidcalibration data with reduced subject specific spectral attributes foreach calibration subject is modified by subtracting a combination ofspectral data from a stored spectral library based on matching thecalibration subject's indirect measurement with a stored measurement insaid spectral library.
 40. The method of claim 37, wherein thecalibration data set is modified to reduce variations in the directmeasurements of the biological attribute due to subject specificattributes for each subject.
 41. The method of claim 37, wherein thecalibration data set is modified to reduce variations in the indirectmeasurements of the biological attributes due to subject specificattributes for each calibration subject.
 42. The method of claim 37,further including forming a prediction model from the direct andindirect measurements of the biological attributes for each subject. 43.The method of claim 37, wherein the specific subject is not one of thecalibration subjects.
 44. The method of claim 37, wherein modifying saidcalibration data set to reduce subject specific spectral attributes foreach calibration subject in said calibration data set includes formingthe mean indirect measurement and mean direct measurement for eachcalibration subject based on the number of measurements from thatcalibration subject followed by mean centering the indirect measurementby subject by subtracting the mean direct measurement from each directmeasurement for each calibration subject.
 45. The method of claim 37,wherein said calibration data with reduced subject specific spectralattributes for each subject is modified by subtracting the mean of thefirst S indirect measurements and direct measurements from a particularsubject from each of the subject's indirect measurements, where S isless than the total number of indirect and direct measurements made onthat subject in generating the calibration data set.
 46. The method ofclaim 37, wherein said calibration data with reduced subject specificspectral attributes for each subject is modified by subtracting the meanof the S nearest in time indirect and direct measurements of a subjectfrom each of the subject's indirect and direct measurements, where S isless than the total number of indirect measurements used in forming thecalibration data set.
 47. The method of claim 37, wherein saidcalibration data with reduced subject specific spectral attributes foreeach subject is modified by subtracting each of a subjects direct andindirect measurements from every other direct and indirect measurementmade on that subject on a round-robin fashion.
 48. A non-invasive methodfor measuring a biological attribute in human tissue of a specificsubject comprising the steps of:(a) providing an apparatus for measuringinfrared absorption, said apparatus including an energy source emittinginfrared energy at multiple wavelengths operatively connected to aninput element, said apparatus further including an output elementoperatively connected to a spectrum analyzer; (b) coupling said inputand output elements to said human tissue; (c) irradiating said tissuethrough said input element with multiple wavelengths of infrared energywith resulting differential absorption of at least some of saidwavelengths; (d) collecting at least a portion of the non-absorbedinfrared energy with said output element followed by determining theintensities of said wavelengths of the non-absorbed infrared energy; and(e) predicting the biological attribute of said specific subjectutilizing a model, wherein said subject specific prediction method usesspectroscopic variation from multiple subjects and one or more referencemeasurements from said specific subject, each of said referencemeasurements including spectroscopic and corresponding directmeasurement of said biological attribute.
 49. A quantitative analysisinstrument for non-invasive measurement of a biological attribute inhuman tissue of a specific subject, said instrument comprising:(a) asource of multiple wavelengths of infrared energy; (b) an input sensorelement for directing said wavelengths of infrared energy into saidtissue and an output sensor element for collecting at least a portion ofthe non-absorbed diffusely reflected infrared energy from said tissue,said input and said output sensors adapted to couple to the surface ofsaid tissue; (c) at least one detector for measuring the intensities ofat least a portion of said wavelengths collected by said output sensorelement; and (d) electronics for processing said measured intensitiesand indicating a value for said biological attribute, said electronicsincluding a processing method incorporated therein, said methodutilizing calibration data which has been developed in a manner thatreduces subject specific spectral attributes and said method utilizesone or more reference measurements from said specific subject.
 50. Theinstrument of claim 49, wherein the electronics for processing one ormore reference measurements from said specific subjects uses saidreference measurements to remove the specific subject attributes fromsaid measured intensities.
 51. The instrument of claim 49, wherein theprocessing method combines said reference measurements with saidcalibration data to create a subject specific model.
 52. The instrumentof claim 49, wherein multiple subjects are used for development of thecalibration data.
 53. The instrument of claim 52, wherein the multiplesubject spectra are processed to reduce subject specific attributes. 54.The instrument of claim 53, within the multiple subject spectra withreduced subject specific attributes are created by subtracting somelinear combination of each subject's spectra from the same subject'sspectra.
 55. An instrument for the non-invasive measurement of abiological attribute for a specific subject, comprising:(a) a memoryadapted to store a calibration data set of direct and indirectmeasurements of the biological attribute obtained from a plurality ofcalibration subjects, wherein the calibration data set has been modifiedto reduce variations therein due to subject specific attributes for eachcalibration subject; (b) means for developing a subject-specificcalibration model from said modified calibration data set wherein saiddata set is tailored for the specific subject with at least onereference measurement of the biological attribute from the specificsubject; (c) means for obtaining at least one indirect measurement ofthe biological attribute from the specific subject; and (d) means forobtaining a measurement of the biological attribute for the specificsubject using the subject-specific calibration model and at least oneindirect measurement of the biological attribute for the specificsubject.
 56. The instrument of claim 55, wherein the electronics forprocessing one or more references measurement from said specificsubjects uses a process that incorporates both the referencemeasurements and said calibration data to generate prediction resultsfrom said measured intensities.
 57. The instrument of claim 55, whereinthe electronics for processing one or more references measurement fromsaid specific subjects uses a process that incorporates both thereference measurements and said measured intensities for generation of aprediction result.
 58. The instrument of claim 55, wherein the referencemeasurement is a direct measurement of the biological attribute for thespecific subject.
 59. The instrument of claim 55, wherein thecalibration data comprises spectroscopic measures of the biologicalattributes.
 60. The instrument of claim 55, wherein the calibration dataset reduced variations in subject specific attributes are created bysubtracting a linear combination of each subject's spectra from the samesubject's spectra.
 61. A method for predicting a variable,comprising:(a) obtaining a calibration data set of direct measurementsand indirect spectral measurements of the variable from a plurality ofenvironments, wherein the calibration data set has been modified toreduce variations therein due to environment-specific attributes foreach environment; (b) developing a environment-specific calibrationmodel from said modified calibration data set tailored for the specificenvironment with at least one reference measurement of the variable fromthe specific environment; (c) obtaining at least one indirectmeasurement of the variable for the specific environment; and (d) usingthe said environment-specific calibration model and said "at least one"indirect measurement of the variable for the specific environment topredict a measure of the variable in the specific environment.
 62. Thevariable of claim 61, wherein the variable is a chemical or biologicalpollutant.
 63. The environment of claim 61, wherein the environment is achemical reactor.
 64. The environment of claim 61, wherein theenvironment is a geophysical environment.